
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sweetviz as sv


data = pd.read_excel('E:\IUST\ترم 8\قابلیت اطمینان انسانی\\Dataset for HRA.xlsx')


data.head()


data.tail()


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 479 entries, 0 to 478
Data columns (total 28 columns):
 #   Column                              Non-Null Count  Dtype  
---  ------                              --------------  -----  
 0   EventId                             479 non-null    object 
 1   Performance-Based Errors            479 non-null    int64  
 2   Judgment & Decision-Making Errors   479 non-null    int64  
 3   Violations                          479 non-null    int64  
 4   Teamwork                            479 non-null    int64  
 5   Mental Awareness                    479 non-null    int64  
 6   State of Mind                       479 non-null    int64  
 7   Physical Problems                   479 non-null    int64  
 8   Sensory Misperception               479 non-null    int64  
 9   Physical Environment                479 non-null    int64  
 10  Technological Environment           479 non-null    int64  
 11  Inadequate Supervision              479 non-null    int64  
 12  Planned Inappropriate Operations    479 non-null    int64  
 13  Supervisory Violations              479 non-null    int64  
 14  Resource Problems                   479 non-null    int64  
 15  Personnel Selection & Staffing      479 non-null    int64  
 16  Climate/ Culture Influences         479 non-null    int64  
 17  Policy & Process Issues             479 non-null    int64  
 18  Technology Failure                  479 non-null    int64  
 19  Acts                                479 non-null    int64  
 20  Preconditions                       479 non-null    int64  
 21  Supervision                         479 non-null    int64  
 22  Organization                        479 non-null    int64  
 23  Fatal or Serious                    479 non-null    int64  
 24  Flight Segment 1=Taxi               479 non-null    int64  
 25  91=1/121=0                          479 non-null    int64  
 26  91= namely general aviation pilots  0 non-null      float64
 27  121 = qualified pilots              0 non-null      float64
dtypes: float64(2), int64(25), object(1)
memory usage: 104.9+ KB


data.describe()


data.columns

Index(['EventId', 'Performance-Based Errors',
       'Judgment & Decision-Making Errors', 'Violations', 'Teamwork',
       'Mental Awareness', 'State of Mind', 'Physical Problems',
       'Sensory Misperception', 'Physical Environment',
       'Technological Environment', 'Inadequate Supervision',
       'Planned Inappropriate Operations', 'Supervisory Violations',
       'Resource Problems', 'Personnel Selection & Staffing',
       'Climate/ Culture Influences', 'Policy & Process Issues',
       'Technology Failure', 'Acts', 'Preconditions', 'Supervision',
       'Organization', 'Fatal or Serious', 'Flight Segment 1=Taxi',
       '91=1/121=0', '91= namely general aviation pilots',
       '121 = qualified pilots'],
      dtype='object')


data.drop(['91= namely general aviation pilots','121 = qualified pilots'],axis=1,inplace=True)


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 479 entries, 0 to 478
Data columns (total 26 columns):
 #   Column                             Non-Null Count  Dtype 
---  ------                             --------------  ----- 
 0   EventId                            479 non-null    object
 1   Performance-Based Errors           479 non-null    int64 
 2   Judgment & Decision-Making Errors  479 non-null    int64 
 3   Violations                         479 non-null    int64 
 4   Teamwork                           479 non-null    int64 
 5   Mental Awareness                   479 non-null    int64 
 6   State of Mind                      479 non-null    int64 
 7   Physical Problems                  479 non-null    int64 
 8   Sensory Misperception              479 non-null    int64 
 9   Physical Environment               479 non-null    int64 
 10  Technological Environment          479 non-null    int64 
 11  Inadequate Supervision             479 non-null    int64 
 12  Planned Inappropriate Operations   479 non-null    int64 
 13  Supervisory Violations             479 non-null    int64 
 14  Resource Problems                  479 non-null    int64 
 15  Personnel Selection & Staffing     479 non-null    int64 
 16  Climate/ Culture Influences        479 non-null    int64 
 17  Policy & Process Issues            479 non-null    int64 
 18  Technology Failure                 479 non-null    int64 
 19  Acts                               479 non-null    int64 
 20  Preconditions                      479 non-null    int64 
 21  Supervision                        479 non-null    int64 
 22  Organization                       479 non-null    int64 
 23  Fatal or Serious                   479 non-null    int64 
 24  Flight Segment 1=Taxi              479 non-null    int64 
 25  91=1/121=0                         479 non-null    int64 
dtypes: int64(25), object(1)
memory usage: 97.4+ KB


data['EventId'].nunique()

477


data[data['EventId'].duplicated()]


data[data['EventId']=='20090604X25647']


data.drop(157,axis=0,inplace=True)


data[data['EventId']=='20090604X25647']


data[data['EventId']=='20110518X94643']


data.drop(303,axis=0,inplace=True)


data[data['EventId']=='20110518X94643']


data['EventId'].nunique()

477


data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 477 entries, 0 to 478
Data columns (total 26 columns):
 #   Column                             Non-Null Count  Dtype 
---  ------                             --------------  ----- 
 0   EventId                            477 non-null    object
 1   Performance-Based Errors           477 non-null    int64 
 2   Judgment & Decision-Making Errors  477 non-null    int64 
 3   Violations                         477 non-null    int64 
 4   Teamwork                           477 non-null    int64 
 5   Mental Awareness                   477 non-null    int64 
 6   State of Mind                      477 non-null    int64 
 7   Physical Problems                  477 non-null    int64 
 8   Sensory Misperception              477 non-null    int64 
 9   Physical Environment               477 non-null    int64 
 10  Technological Environment          477 non-null    int64 
 11  Inadequate Supervision             477 non-null    int64 
 12  Planned Inappropriate Operations   477 non-null    int64 
 13  Supervisory Violations             477 non-null    int64 
 14  Resource Problems                  477 non-null    int64 
 15  Personnel Selection & Staffing     477 non-null    int64 
 16  Climate/ Culture Influences        477 non-null    int64 
 17  Policy & Process Issues            477 non-null    int64 
 18  Technology Failure                 477 non-null    int64 
 19  Acts                               477 non-null    int64 
 20  Preconditions                      477 non-null    int64 
 21  Supervision                        477 non-null    int64 
 22  Organization                       477 non-null    int64 
 23  Fatal or Serious                   477 non-null    int64 
 24  Flight Segment 1=Taxi              477 non-null    int64 
 25  91=1/121=0                         477 non-null    int64 
dtypes: int64(25), object(1)
memory usage: 100.6+ KB


plt.figure(figsize=(8,5))
sns.heatmap(data=data.isnull(),cbar=False,yticklabels=False,cmap='viridis')

<AxesSubplot:>


data.isnull().sum()

EventId                              0
Performance-Based Errors             0
Judgment & Decision-Making Errors    0
Violations                           0
Teamwork                             0
Mental Awareness                     0
State of Mind                        0
Physical Problems                    0
Sensory Misperception                0
Physical Environment                 0
Technological Environment            0
Inadequate Supervision               0
Planned Inappropriate Operations     0
Supervisory Violations               0
Resource Problems                    0
Personnel Selection & Staffing       0
Climate/ Culture Influences          0
Policy & Process Issues              0
Technology Failure                   0
Acts                                 0
Preconditions                        0
Supervision                          0
Organization                         0
Fatal or Serious                     0
Flight Segment 1=Taxi                0
91=1/121=0                           0
dtype: int64


data['Performance-Based Errors'].value_counts()

0    249
1    228
Name: Performance-Based Errors, dtype: int64


data['Judgment & Decision-Making Errors'].value_counts()

0    416
1     61
Name: Judgment & Decision-Making Errors, dtype: int64


data['Violations'].value_counts()

0    443
1     34
Name: Violations, dtype: int64


data['Teamwork'].value_counts()

0    460
1     17
Name: Teamwork, dtype: int64


data['Mental Awareness'].value_counts()

0    469
1      8
Name: Mental Awareness, dtype: int64


data['State of Mind'].value_counts()

0    475
1      2
Name: State of Mind, dtype: int64


data['Physical Problems'].value_counts()

0    458
1     19
Name: Physical Problems, dtype: int64


data['Sensory Misperception'].value_counts()

0    468
1      9
Name: Sensory Misperception, dtype: int64


data['Physical Environment'].value_counts()

0    399
1     78
Name: Physical Environment, dtype: int64


data['Technological Environment'].value_counts()

0    476
1      1
Name: Technological Environment, dtype: int64


data['Inadequate Supervision'].value_counts()

0    456
1     21
Name: Inadequate Supervision, dtype: int64


data['Planned Inappropriate Operations'].value_counts()

0    476
1      1
Name: Planned Inappropriate Operations, dtype: int64


data['Supervisory Violations'].value_counts()

0    477
Name: Supervisory Violations, dtype: int64


data['Resource Problems'].value_counts()

0    470
1      7
Name: Resource Problems, dtype: int64


data['Personnel Selection & Staffing'].value_counts()

0    477
Name: Personnel Selection & Staffing, dtype: int64


data['Climate/ Culture Influences'].value_counts()

0    477
Name: Climate/ Culture Influences, dtype: int64


data['Policy & Process Issues'].value_counts()

0    461
1     16
Name: Policy & Process Issues, dtype: int64


data['Technology Failure'].value_counts()

0    324
1    153
Name: Technology Failure, dtype: int64


data['Acts'].value_counts()

1    304
0    173
Name: Acts, dtype: int64


data['Preconditions'].value_counts()

0    356
1    121
Name: Preconditions, dtype: int64


data['Supervision'].value_counts()

0    455
1     22
Name: Supervision, dtype: int64


data['Organization'].value_counts()

0    454
1     23
Name: Organization, dtype: int64


data['Fatal or Serious'].value_counts()

0    307
1    170
Name: Fatal or Serious, dtype: int64


data['Flight Segment 1=Taxi'].value_counts()

7    170
2    115
6     81
5     43
4     35
3     33
Name: Flight Segment 1=Taxi, dtype: int64


data['91=1/121=0'].value_counts()

1    325
0    152
Name: 91=1/121=0, dtype: int64


data.head()


data.drop(['EventId','Teamwork' , 'Mental Awareness' , 'State of Mind' , 'Physical Problems' , 'Sensory Misperception' ,
           'Technological Environment' , 'Planned Inappropriate Operations' , 'Supervisory Violations' , 
           'Resource Problems' , 'Personnel Selection & Staffing' , 'Climate/ Culture Influences' , 'Policy & Process Issues'],
         axis=1,inplace=True)


data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 477 entries, 0 to 478
Data columns (total 13 columns):
 #   Column                             Non-Null Count  Dtype
---  ------                             --------------  -----
 0   Performance-Based Errors           477 non-null    int64
 1   Judgment & Decision-Making Errors  477 non-null    int64
 2   Violations                         477 non-null    int64
 3   Physical Environment               477 non-null    int64
 4   Inadequate Supervision             477 non-null    int64
 5   Technology Failure                 477 non-null    int64
 6   Acts                               477 non-null    int64
 7   Preconditions                      477 non-null    int64
 8   Supervision                        477 non-null    int64
 9   Organization                       477 non-null    int64
 10  Fatal or Serious                   477 non-null    int64
 11  Flight Segment 1=Taxi              477 non-null    int64
 12  91=1/121=0                         477 non-null    int64
dtypes: int64(13)
memory usage: 52.2 KB


data.head()


sns.pairplot(data , hue='Fatal or Serious', palette='Set1')

<seaborn.axisgrid.PairGrid at 0x262e9d69640>


plt.figure(figsize=(12,8))
sns.heatmap(data.corr(),cmap='coolwarm')

<AxesSubplot:>


#analyzing the dataset
advert_report = sv.analyze([data,'Accident'],target_feat='Fatal or Serious',pairwise_analysis="on")
#display the report
advert_report.show_notebook(w='100%')


my_report = sv.compare_intra(data, data['Fatal or Serious'] ==1, ["Fatal", "Serious"])
my_report.show_notebook(w='100%')


from fast_ml.model_development import train_valid_test_split


X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(data, target = 'Fatal or Serious', 
                                                                            train_size=0.7, valid_size=0.1, test_size=0.2,
                                                                           random_state=101)

print(X_train.shape), print(y_train.shape)
print(X_valid.shape), print(y_valid.shape)
print(X_test.shape), print(y_test.shape)

(333, 12)
(333,)
(48, 12)
(48,)
(96, 12)
(96,)

(None, None)


X_train


y_train

429    1
333    1
109    0
93     0
229    0
      ..
63     1
328    0
339    0
11     0
353    0
Name: Fatal or Serious, Length: 333, dtype: int64


X_valid


y_valid

114    1
440    0
143    1
226    0
268    0
278    0
138    0
31     0
91     0
406    0
100    1
128    0
315    1
104    1
212    0
169    1
190    1
260    0
438    0
391    0
418    1
133    0
317    0
334    1
117    0
238    0
454    1
213    0
130    0
346    0
349    0
180    0
210    0
419    0
17     1
184    1
351    0
478    0
408    1
13     0
211    1
38     1
72     1
2      0
387    0
179    0
308    1
412    0
Name: Fatal or Serious, dtype: int64


X_test


y_test

455    0
399    0
166    1
188    0
53     0
      ..
4      0
234    0
8      0
468    0
335    1
Name: Fatal or Serious, Length: 96, dtype: int64


X_train_imbalance = X_train.copy()


y_train_imbalance = y_train.copy()


from imblearn.over_sampling import SMOTE
from collections import Counter


oversample = SMOTE()
X_train,y_train = oversample.fit_resample(X_train,y_train)
counter = Counter(y_train)
counter

Counter({1: 206, 0: 206})


from sklearn.linear_model import LogisticRegression


lr = LogisticRegression(random_state=101)


lr.fit(X_train,y_train)

LogisticRegression(random_state=101)

LogisticRegression(random_state=101)


pred = lr.predict(X_test)


print('Score:\n',lr.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred))

Score:
 0.7395833333333334 

Confusion Matrix:
 [[53 17]
 [ 8 18]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.87      0.76      0.81        70
           1       0.51      0.69      0.59        26

    accuracy                           0.74        96
   macro avg       0.69      0.72      0.70        96
weighted avg       0.77      0.74      0.75        96


mat_T = confusion_matrix(y_test,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


df_eval = pd.DataFrame({'Label':y_test,
                        'Prediction': pred})
df_eval


from sklearn.preprocessing import StandardScaler


scaler = StandardScaler()


scaler.fit(data.drop(['Fatal or Serious'],axis=1))

StandardScaler()

StandardScaler()


scaled_features = scaler.transform(data.drop(['Fatal or Serious'],axis=1))


df_feat = pd.DataFrame(scaled_features,columns = data.columns[:-1])
df_feat


from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train,y_train)
pred = knn.predict(X_valid)
print('Score:\n',knn.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.6666666666666666 

Confusion Matrix:
 [[22  9]
 [ 7 10]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.76      0.71      0.73        31
           1       0.53      0.59      0.56        17

    accuracy                           0.67        48
   macro avg       0.64      0.65      0.64        48
weighted avg       0.68      0.67      0.67        48


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


error_rate = []
for i in range(1,40):
    knn = KNeighborsClassifier(n_neighbors= i)
    knn.fit(X_train,y_train)
    pred_i = knn.predict(X_valid)
    error_rate.append(np.mean(pred_i != y_valid))


plt.figure(figsize=(10,6))
plt.plot(range(1,40),error_rate,color='blue',ls='--',marker='o',markerfacecolor='red',markersize=10)
plt.title('ERROR RATE VS. K VALUES')
plt.xlabel('K')
plt.ylabel('ERROR RATE')

Text(0, 0.5, 'ERROR RATE')


from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=9)
knn.fit(X_train,y_train)
pred = knn.predict(X_valid)
print('Score:\n',knn.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.7083333333333334 

Confusion Matrix:
 [[21 10]
 [ 4 13]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.84      0.68      0.75        31
           1       0.57      0.76      0.65        17

    accuracy                           0.71        48
   macro avg       0.70      0.72      0.70        48
weighted avg       0.74      0.71      0.71        48


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from sklearn.tree import DecisionTreeClassifier


dt = DecisionTreeClassifier(random_state=101)


dt.fit(X_train,y_train)

DecisionTreeClassifier(random_state=101)

DecisionTreeClassifier(random_state=101)


pred = dt.predict(X_valid)


print('Score:\n',dt.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.7291666666666666 

Confusion Matrix:
 [[24  7]
 [ 6 11]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.80      0.77      0.79        31
           1       0.61      0.65      0.63        17

    accuracy                           0.73        48
   macro avg       0.71      0.71      0.71        48
weighted avg       0.73      0.73      0.73        48


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


df_eval = pd.DataFrame({'Label':y_valid,
                        'Prediction': pred})
df_eval


from sklearn.tree import plot_tree
plt.figure(figsize=(50,40))
plot_tree(dt, filled=True)
plt.title("Decision Tree")
plt.savefig('Decision Tree Visualize.jpg')


pd.DataFrame(data=dt.feature_importances_,index=X_train.columns,columns=['Value']).sort_values(by='Value',ascending=False)


param_grid = {'criterion':['gini','entropy'],'splitter':['best', 'random']}


from sklearn.model_selection import GridSearchCV
grid1 = GridSearchCV(DecisionTreeClassifier(random_state=101),param_grid,refit=True,verbose=3)
grid1.fit(X_train,y_train)
pred = grid1.predict(X_valid)

Fitting 5 folds for each of 4 candidates, totalling 20 fits
[CV 1/5] END .....criterion=gini, splitter=best;, score=0.783 total time=   0.0s
[CV 2/5] END .....criterion=gini, splitter=best;, score=0.675 total time=   0.0s
[CV 3/5] END .....criterion=gini, splitter=best;, score=0.793 total time=   0.0s
[CV 4/5] END .....criterion=gini, splitter=best;, score=0.780 total time=   0.0s
[CV 5/5] END .....criterion=gini, splitter=best;, score=0.744 total time=   0.0s
[CV 1/5] END ...criterion=gini, splitter=random;, score=0.783 total time=   0.0s
[CV 2/5] END ...criterion=gini, splitter=random;, score=0.687 total time=   0.0s
[CV 3/5] END ...criterion=gini, splitter=random;, score=0.817 total time=   0.0s
[CV 4/5] END ...criterion=gini, splitter=random;, score=0.793 total time=   0.0s
[CV 5/5] END ...criterion=gini, splitter=random;, score=0.744 total time=   0.0s
[CV 1/5] END ..criterion=entropy, splitter=best;, score=0.783 total time=   0.0s
[CV 2/5] END ..criterion=entropy, splitter=best;, score=0.687 total time=   0.0s
[CV 3/5] END ..criterion=entropy, splitter=best;, score=0.793 total time=   0.0s
[CV 4/5] END ..criterion=entropy, splitter=best;, score=0.780 total time=   0.0s
[CV 5/5] END ..criterion=entropy, splitter=best;, score=0.744 total time=   0.0s
[CV 1/5] END criterion=entropy, splitter=random;, score=0.819 total time=   0.0s
[CV 2/5] END criterion=entropy, splitter=random;, score=0.687 total time=   0.0s
[CV 3/5] END criterion=entropy, splitter=random;, score=0.793 total time=   0.0s
[CV 4/5] END criterion=entropy, splitter=random;, score=0.793 total time=   0.0s
[CV 5/5] END criterion=entropy, splitter=random;, score=0.732 total time=   0.0s


grid1.best_params_

{'criterion': 'gini', 'splitter': 'random'}


print('Score:\n',grid1.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.7708333333333334 

Confusion Matrix:
 [[24  7]
 [ 4 13]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.86      0.77      0.81        31
           1       0.65      0.76      0.70        17

    accuracy                           0.77        48
   macro avg       0.75      0.77      0.76        48
weighted avg       0.78      0.77      0.77        48


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from sklearn.ensemble import RandomForestClassifier


rf = RandomForestClassifier(n_estimators=100,random_state=101)


rf.fit(X_train,y_train)

RandomForestClassifier(random_state=101)

RandomForestClassifier(random_state=101)


pred_rf = rf.predict(X_valid)


df_eval = pd.DataFrame({'Label':y_valid,
                        'Prediction': pred_rf
                        })
df_eval


print('Score:\n',rf.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred_rf),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred_rf))

Score:
 0.75 

Confusion Matrix:
 [[24  7]
 [ 5 12]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.83      0.77      0.80        31
           1       0.63      0.71      0.67        17

    accuracy                           0.75        48
   macro avg       0.73      0.74      0.73        48
weighted avg       0.76      0.75      0.75        48


mat_T = confusion_matrix(y_valid,pred_rf)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


pd.DataFrame(data=rf.feature_importances_,index=X_train.columns,columns=['Value']).sort_values(by='Value',ascending=False)


from sklearn.svm import SVC


svm = SVC(random_state=101)


svm.fit(X_train,y_train)

SVC(random_state=101)

SVC(random_state=101)


pred = svm.predict(X_valid)


df_eval = pd.DataFrame({'Label':y_valid,
                        'Prediction': pred
                        })
df_eval


print('Score:\n',svm.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.7708333333333334 

Confusion Matrix:
 [[21 10]
 [ 1 16]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.95      0.68      0.79        31
           1       0.62      0.94      0.74        17

    accuracy                           0.77        48
   macro avg       0.78      0.81      0.77        48
weighted avg       0.83      0.77      0.78        48


df_eval[df_eval['Label'] != df_eval['Prediction']]


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from sklearn.naive_bayes import BernoulliNB
bnb = BernoulliNB()
bnb.fit(X_train, y_train)
pred = bnb.predict(X_valid)


print("Number of mislabeled points out of a total %d points : %d" % (X_valid.shape[0], (y_valid != pred).sum()))

Number of mislabeled points out of a total 48 points : 19


print('Score:\n',bnb.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.6041666666666666 

Confusion Matrix:
 [[22  9]
 [10  7]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.69      0.71      0.70        31
           1       0.44      0.41      0.42        17

    accuracy                           0.60        48
   macro avg       0.56      0.56      0.56        48
weighted avg       0.60      0.60      0.60        48


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from sklearn.ensemble import BaggingClassifier


bca = BaggingClassifier(SVC(random_state=101),random_state=101)


bca.fit(X_train, y_train)

BaggingClassifier(estimator=SVC(random_state=101), random_state=101)

BaggingClassifier(estimator=SVC(random_state=101), random_state=101)

SVC(random_state=101)

SVC(random_state=101)


pred = bca.predict(X_valid)


print('Score:\n',bca.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.7708333333333334 

Confusion Matrix:
 [[21 10]
 [ 1 16]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.95      0.68      0.79        31
           1       0.62      0.94      0.74        17

    accuracy                           0.77        48
   macro avg       0.78      0.81      0.77        48
weighted avg       0.83      0.77      0.78        48


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True, fmt='d', cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from sklearn.ensemble import GradientBoostingClassifier


gbc = GradientBoostingClassifier(random_state=101)


gbc.fit(X_train, y_train)

GradientBoostingClassifier(random_state=101)

GradientBoostingClassifier(random_state=101)


pred = gbc.predict(X_valid)


print('Score:\n',gbc.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.7916666666666666 

Confusion Matrix:
 [[25  6]
 [ 4 13]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.86      0.81      0.83        31
           1       0.68      0.76      0.72        17

    accuracy                           0.79        48
   macro avg       0.77      0.79      0.78        48
weighted avg       0.80      0.79      0.79        48


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True, fmt='d', cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from xgboost import XGBClassifier


from sklearn.metrics import accuracy_score


xgb = XGBClassifier()


xgb.fit(X_train,y_train)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)


pred = xgb.predict(X_valid)


predictions = [round(value) for value in pred]


accuracy = accuracy_score(y_valid, predictions)
print("Accuracy: %.3f%%" % (accuracy * 100.0))

Accuracy: 77.083%


print('Score:\n',xgb.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.7708333333333334 

Confusion Matrix:
 [[25  6]
 [ 5 12]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.83      0.81      0.82        31
           1       0.67      0.71      0.69        17

    accuracy                           0.77        48
   macro avg       0.75      0.76      0.75        48
weighted avg       0.77      0.77      0.77        48


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True, fmt='d', cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from sklearn.ensemble import VotingClassifier


model_1 = SVC(random_state=101)
model_1.fit(X_train, y_train)
pred_1 = model_1.predict(X_valid)


model_2 = GradientBoostingClassifier(random_state=101)
model_2.fit(X_train, y_train)
pred_2 = model_2.predict(X_valid)


model_3 = RandomForestClassifier(n_estimators=100,random_state=101)
model_3.fit(X_train, y_train)
pred_3 = model_3.predict(X_valid)


eclf = VotingClassifier(estimators=[('Support Vector Machine', model_1),
                                    ('Gradient Boosting', model_2),
                                    ('Random Forest', model_3)]
                        ,voting='hard')


from sklearn.model_selection import cross_val_score


for clf, label in zip([model_1, model_2, model_3, eclf], ['Support Vector Machine', 'Gradient Boosting' , 'Random Forest', 
                                                          'Ensemble']):

    scores = cross_val_score(clf, X_valid, y_valid, scoring='accuracy', cv=5)

    print("Accuracy: %0.3f (+/- %0.3f) [%s]" % (scores.mean(), scores.std(), label))

Accuracy: 0.607 (+/- 0.108) [Support Vector Machine]
Accuracy: 0.631 (+/- 0.199) [Gradient Boosting]
Accuracy: 0.753 (+/- 0.147) [Random Forest]
Accuracy: 0.733 (+/- 0.159) [Ensemble]


X_trainValid = pd.concat([X_train,X_valid],axis=0)


X_trainValid


y_trainValid = pd.concat([y_train,y_valid],axis=0)


y_trainValid

0      1
1      1
2      0
3      0
4      0
      ..
2      0
387    0
179    0
308    1
412    0
Name: Fatal or Serious, Length: 460, dtype: int64


model_0 = LogisticRegression(random_state=101)
model_0.fit(X_trainValid, y_trainValid)

LogisticRegression(random_state=101)

LogisticRegression(random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_0, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.63043478 0.58695652 0.63043478 0.73188406 0.67391304]
0.651 accuracy with a standard deviation of 0.049


pred_0 = model_0.predict(X_test)


print('Score:\n',model_0.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_0),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_0))

Score:
 0.7604166666666666 

Confusion Matrix:
 [[56 14]
 [ 9 17]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.86      0.80      0.83        70
           1       0.55      0.65      0.60        26

    accuracy                           0.76        96
   macro avg       0.70      0.73      0.71        96
weighted avg       0.78      0.76      0.77        96


model_1 = RandomForestClassifier(n_estimators=100,random_state=101)
model_1.fit(X_trainValid, y_trainValid)

RandomForestClassifier(random_state=101)

RandomForestClassifier(random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_1, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.73913043 0.77536232 0.7173913  0.73913043 0.7173913 ]
0.738 accuracy with a standard deviation of 0.021


pred_1 = model_1.predict(X_test)


print('Score:\n',model_1.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_1),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_1))

Score:
 0.8125 

Confusion Matrix:
 [[56 14]
 [ 4 22]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.93      0.80      0.86        70
           1       0.61      0.85      0.71        26

    accuracy                           0.81        96
   macro avg       0.77      0.82      0.79        96
weighted avg       0.85      0.81      0.82        96


model_2 = XGBClassifier()
model_2.fit(X_trainValid, y_trainValid)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_2, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.76086957 0.76086957 0.71014493 0.73913043 0.73188406]
0.741 accuracy with a standard deviation of 0.019


pred_2 = model_2.predict(X_test)


print('Score:\n',model_2.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_2),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_2))

Score:
 0.8020833333333334 

Confusion Matrix:
 [[56 14]
 [ 5 21]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.92      0.80      0.85        70
           1       0.60      0.81      0.69        26

    accuracy                           0.80        96
   macro avg       0.76      0.80      0.77        96
weighted avg       0.83      0.80      0.81        96


model_3 = GradientBoostingClassifier(random_state=101)
model_3.fit(X_trainValid, y_trainValid)

GradientBoostingClassifier(random_state=101)

GradientBoostingClassifier(random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_3, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.73913043 0.77536232 0.71014493 0.73913043 0.73913043]
0.741 accuracy with a standard deviation of 0.021


pred_3 = model_3.predict(X_test)


print('Score:\n',model_3.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_3),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_3))

Score:
 0.8020833333333334 

Confusion Matrix:
 [[57 13]
 [ 6 20]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.90      0.81      0.86        70
           1       0.61      0.77      0.68        26

    accuracy                           0.80        96
   macro avg       0.76      0.79      0.77        96
weighted avg       0.82      0.80      0.81        96


model_4 = BaggingClassifier()
model_4.fit(X_trainValid, y_trainValid)

BaggingClassifier()

BaggingClassifier()


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_4, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.75362319 0.77536232 0.7173913  0.73913043 0.69565217]
0.736 accuracy with a standard deviation of 0.028


pred_4 = model_4.predict(X_test)


print('Score:\n',model_4.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_4),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_4))

Score:
 0.7395833333333334 

Confusion Matrix:
 [[54 16]
 [ 9 17]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.86      0.77      0.81        70
           1       0.52      0.65      0.58        26

    accuracy                           0.74        96
   macro avg       0.69      0.71      0.69        96
weighted avg       0.76      0.74      0.75        96


model_5 = SVC(random_state=101)
model_5.fit(X_trainValid, y_trainValid)

SVC(random_state=101)

SVC(random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_5, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.77536232 0.75362319 0.7173913  0.70289855 0.74637681]
0.739 accuracy with a standard deviation of 0.026


pred_5 = model_5.predict(X_test)


print('Score:\n',model_5.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_5),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_5))

Score:
 0.6979166666666666 

Confusion Matrix:
 [[49 21]
 [ 8 18]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.86      0.70      0.77        70
           1       0.46      0.69      0.55        26

    accuracy                           0.70        96
   macro avg       0.66      0.70      0.66        96
weighted avg       0.75      0.70      0.71        96


model_6 = DecisionTreeClassifier(random_state=101)
model_6.fit(X_trainValid, y_trainValid)

DecisionTreeClassifier(random_state=101)

DecisionTreeClassifier(random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_6, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.71014493 0.75362319 0.6884058  0.73188406 0.68115942]
0.713 accuracy with a standard deviation of 0.027


pred_6 = model_6.predict(X_test)


print('Score:\n',model_6.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_6),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_6))

Score:
 0.8020833333333334 

Confusion Matrix:
 [[56 14]
 [ 5 21]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.92      0.80      0.85        70
           1       0.60      0.81      0.69        26

    accuracy                           0.80        96
   macro avg       0.76      0.80      0.77        96
weighted avg       0.83      0.80      0.81        96


model_7 = BernoulliNB()
model_7.fit(X_trainValid, y_trainValid)

BernoulliNB()

BernoulliNB()


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_7, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.60869565 0.57246377 0.65942029 0.66666667 0.5942029 ]
0.620 accuracy with a standard deviation of 0.037


pred_7 = model_7.predict(X_test)


print('Score:\n',model_7.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_7),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_7))

Score:
 0.7708333333333334 

Confusion Matrix:
 [[59 11]
 [11 15]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.84      0.84      0.84        70
           1       0.58      0.58      0.58        26

    accuracy                           0.77        96
   macro avg       0.71      0.71      0.71        96
weighted avg       0.77      0.77      0.77        96


eclf = VotingClassifier(estimators=[('XGboost Classifier', model_2),
                                    ('Gradient Boosting Classifier', model_3),
                                    ('Random Forest Classifier', model_1)]
                        ,voting='hard')
eclf.fit(X_trainValid, y_trainValid)

VotingClassifier(estimators=[('XGboost Classifier',
                              XGBClassifier(base_score=None, booster=None,
                                            callbacks=None,
                                            colsample_bylevel=None,
                                            colsample_bynode=None,
                                            colsample_bytree=None,
                                            early_stopping_rounds=None,
                                            enable_categorical=False,
                                            eval_metric=None,
                                            feature_types=None, gamma=None,
                                            gpu_id=None, grow_policy=None,
                                            importance_type=None,
                                            interaction_constraints=No...
                                            max_cat_to_onehot=None,
                                            max_delta_step=None, max_depth=None,
                                            max_leaves=None,
                                            min_child_weight=None, missing=nan,
                                            monotone_constraints=None,
                                            n_estimators=100, n_jobs=None,
                                            num_parallel_tree=None,
                                            predictor=None, random_state=None, ...)),
                             ('Gradient Boosting Classifier',
                              GradientBoostingClassifier(random_state=101)),
                             ('Random Forest Classifier',
                              RandomForestClassifier(random_state=101))])

VotingClassifier(estimators=[('XGboost Classifier',
                              XGBClassifier(base_score=None, booster=None,
                                            callbacks=None,
                                            colsample_bylevel=None,
                                            colsample_bynode=None,
                                            colsample_bytree=None,
                                            early_stopping_rounds=None,
                                            enable_categorical=False,
                                            eval_metric=None,
                                            feature_types=None, gamma=None,
                                            gpu_id=None, grow_policy=None,
                                            importance_type=None,
                                            interaction_constraints=No...
                                            max_cat_to_onehot=None,
                                            max_delta_step=None, max_depth=None,
                                            max_leaves=None,
                                            min_child_weight=None, missing=nan,
                                            monotone_constraints=None,
                                            n_estimators=100, n_jobs=None,
                                            num_parallel_tree=None,
                                            predictor=None, random_state=None, ...)),
                             ('Gradient Boosting Classifier',
                              GradientBoostingClassifier(random_state=101)),
                             ('Random Forest Classifier',
                              RandomForestClassifier(random_state=101))])

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)

GradientBoostingClassifier(random_state=101)

RandomForestClassifier(random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(eclf, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.75362319 0.7826087  0.7173913  0.75362319 0.74637681]
0.751 accuracy with a standard deviation of 0.021


pred_8 = eclf.predict(X_test)


print('Score:\n',eclf.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_8),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_8))

Score:
 0.8020833333333334 

Confusion Matrix:
 [[56 14]
 [ 5 21]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.92      0.80      0.85        70
           1       0.60      0.81      0.69        26

    accuracy                           0.80        96
   macro avg       0.76      0.80      0.77        96
weighted avg       0.83      0.80      0.81        96


X = pd.concat([X_train,X_valid,X_test],axis=0)


y = pd.concat([y_train,y_valid,y_test],axis=0)


from sklearn.model_selection import cross_val_score


from sklearn import metrics


from sklearn.model_selection import ShuffleSplit
n_samples = X_train.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_2, X, y, cv=cv)
scores

array([0.74251497, 0.73652695, 0.76047904, 0.76047904, 0.80838323])


for train_index, test_index in cv.split(X):
    print("%s %s" % (train_index, test_index))

[526 486  69 296 248  41  70 321 376 351  43  36 469 209 424 454 170 227
 143 384 429 527 293 265  53 165  74 231  51 132  44 552 267 470 350 302
 387 525 263 148 154 220 390 133 162 310 372 176 484  15 198  13 175 155
 359 230 335 313 160  17 317 283 153 278 425 475  86  73 483 168   7 389
 187 109 471 396 548 472 211  93 524 465 385 353 364 121 513 346 521 215
   9 482 324 164 152 360 391 323 234 169 474 426  45 334 112  90 126  78
 167 534 328 431 134 281 295  29 543 102 304 238 330 495 496 414 259 158
 210  20 289 345 206 347 123 333 554 439 119 442 377 251 252 401 135 433
 421 247 315 373 245 182 410 531 354 311 479  85 365 159 177 108 242 172
 115 298  35  37  34   1 382 367 542 101 150 487 306 458 332  11 553 223
  25 459 467 408 355  63 192 221 208 464 550 460 461  22  42 348   6 349
 398  88 322  50  80 258 380 193 145  54 232  66 538  26 151 185 445 122
  96 518 190 509  84 144 489 456  87 270 307 146  61 196  68  79 129 299
  67 423 125 492 166 292  97 301 219 547 485 309 417 450 370 476 551 180
 404 280 397 253  52 529 546 428 142 443 184 462 358  77  82 191 217 305
 549  27 505 199 356  94 383 254 235  23  21 501 149 285 453 131  46 441
 338 451 226 207 163 407 244 452 357  71 366 494 545 262 435 120 473 507
 197 250  14 303 340 339   3  28 511 478 113 394 395  92 533  58 555 386
 173 137 214 229 438  47 174 537 508 282 378 342 532 411 276 103 444 499
  10  30 419 498 539 463 203 273 140 362 512 300 327 535 402 528 329 294
 440 436 519 284  99 264 239 457 503 105 343  76 275 249 418  59 371 136
 506  49  40 477   5 110 316 393  75 337 523] [ 98 341  48 161 522 189 515 117 413 290 124  24 361 107  55 446 363 400
   8  81 422 268 279 455 118 540  39  18 336 488 114 493 186 490 466 106
  83 104 434 272 312 286 183 325 412  32 141 520 291 100 261 237 319 530
 139   0 181 241   4 236 369 243 195 399 308 178 200 468 368 202   2 420
 331 218 138 147 257 260  31 500 156 240 271  64 213 504 255  62  12 497
 437 415 326 297 352 432 392 388  19 225 405 502 233 344 416 314 194 517
 256 201  33 491  89 427 130 128 448 406  72 277 510 449 318 287 381  60
  16 188 205 514  65  56 116 447 320 288 375 541 274 379 516 212 157 409
 224  95 480 246 204 179 111 171 430 544 216 127  38 403 269 374  91 222
 266 481  57 228 536]
[360  88 201 350 525 358 153 467 419 530  25  92 300 347  52  68  85 128
 461 285 141 111 267 486 318 158 226 423 274 471 354 162 427 504 495 249
 375 340 386 367  16 555 520 143 189 303 460   6 462 422 333 125 117 477
 402 175 212 156 533 311  76  48 337 270 256 151  71 209  75 147 199 415
  78 107 304 152 283 528 309 407 176 138  42  62 399 553  30 450 380 231
 397  96 323  98  63 493 291 193 166   1 296 276 316  93 349  57 225 106
 532   9 180 384 455 171  95 204 356 287 400 405 330   8 431  97 382 240
 217 232 216 170 535  23 123 142  41 393 108 222 516  43 336 281 325 187
 191 265 136 413 186 131 466 110 221 239 299 104 404 289 161 474 546 207
 383 470 253 198 174 369 469 228 514 203 364 523 390 537 205 396 130 448
 374 184 497 312 118 541 488 317 341 334 511 353 134 398 155  70   5 124
  77 432 308 418 439 344 500 154 305 255  94 435 529 245  81 208 328  10
 366 527 491 372 169 503 177 433 260 213 518  17 202 554 113 220 286  27
 357  40 160 524 190 414 370 445  54 290 543 167 302  11 103 105 430 468
 381 183 457 126 388 227 310 148  21 425 542 250 501 456 133 288 294 420
  44 539 277 373 452 145  84  24 101  73 473 243 129 526  89 368 252 363
 196 275  83 258 480 263 549 150 489 522  13 343 324 376 268  14 115  91
   2 496  50 195 550 257  39 234 515 551 365 434 321 391 362  79 481 332
  38 322 214 266  65 315  64  59  18 371 417 188  29 295 262 192 244 229
  45 215 279 119  66 326   4 314 223  69 206 159 165 146  15 406 436 144
 173 102 426 499 200 487  58 272  80 251 248 438 233 507 428 490 348 442
 444 235 264 338 424 261  53 392 451  86 441] [ 32 301   3 181 379 429 157 377 319 182 498  99 446 492  60 458 421 540
 552 132 219 508 278 320 479 485 329 355 179 378  51 163 331 127 273 313
 509 449 346 510 459  49   0 120 109 454  28 236 506 547 100 548  26 351
 178 464  20 389 335 345  19 538 409  90 385 387 242  72 403 361  22 246
 531 534 440 197 502 292 359 545 307 453 194 410 247 149 271 476  12 339
 218 472 168 269  36 293 437 254  67  33 237  35 116 536 408 494 505 482
 112 519 185 411 342  46 306 164 352 139 280  56 465 114 513 224 259  82
 395 172  37 463  34 121 478 137 517 443   7 483 475 122 241 140  74  31
  87  55 298 394 230 282 484 401 544 327  47 297 210 447  61 416 135 512
 211 521 412 284 238]
[109 379  99 299 274 383 135 218 123 188 142 514  13 328 513 377 315 381
 488 219 525 107 305 268 529 311  82 226 216 356 146 395 325 428 439 466
 496 410  41 250 171 489 336  74 240 258 527 421 441  55 554 312 161 119
 503  64 257 247  28  70 288 223 319  81 192 280 484 332 405 314 338 222
 176  84 371  73 256   3 264 285 479  48  32 254 150 460 526 382 316 321
 447 436 124 508 497 265 398 477 452 294  75 241 144 530  68 419  59 196
   7 438 170 534 420   5 446 450 360 344 423 160  85 253 275 115 174 463
 346 205 372 237  16 148 353 173 357  20 467 231 362  34 537 153 132  45
 168 491  98 485  29 235 448 290 487 246 186 203 252 374 434 553 416 158
 300 276 468 149 156 248 455 298  62 449 507 555 283 334 386 243 427  58
 528 339 538 291 533 211  76 145 307 392 220 376 394   1 459 404 194 114
 550  97 506  83 191 425 502  50 208 520 232 206 213 159 361 151 306 251
 369 154 393 380 175  43 273  72 318 200 470 141 456 229 143 510 179 197
 453 472 317 403 542 199 511 364  61  53 431 366 130 122 413  30 270 375
 105 202 435  25 536 482 540 324  24 345  71  54  33 227 190  36 535 546
  93 548 106  35   4 209  49 351 481 126  31 437 242 512 370  87 471  79
 509 181 464 271 347 545 417 465 279 169 320 391 287 492 541 343 407 385
 500 552 365 498  91  26  69  56   8  15  21 327 519  80 127 433 373 166
  17 272 451 352 116 422 214 522 432 402 310 458  40 445 189 245 277 185
 131 400 313 163 408 462 249  23 289  14 444 354 426  63 349 286  38 217
 399 128 182 233  46 292 267 384 266 152 331 350 120 204 180 387 103 518
  37  47 121  12  94 125 269 139  96 454  27] [136 476 430 184  60 414 230 396 263 494   9   0 293 531 147 281 442 108
 543 411  67 389 102 100 239  11 515  39 342 303 129 201 521 480 284 440
 547 255 330 368  65 164 326 210 388 236 112 499 483 505 475  92 140  77
 549 113 461 111 406 457  10 478 340  90 297 358  88 412 155 296 501 215
 259 183 333  95 329 469 134 221 193 378 532 337 278  52 262 118 516  89
 490 322 137  19 390 260 308 304 110 177 397 443  44 493 424 207 551 409
  78 133 225 282 429 165 238 138 524 335  22 544 261 523 101 295 302 359
 341 234  42 301 418 474  66 309 363 244 212   2 517 117 178 198 355 367
 167  51 157 104 415 504 187  86 195 473 486 172  18 224 495  57 401 539
 323 348 162   6 228]
[111 455 546 375  14 221 393 198  52  69  29  18 173 135  65 321 102 230
 313 351   7 471 240 297 257 401  30  38 326 181 388 457 305 522 256 204
 105 319 194  32  89 165  43 280 489 469 264  23 213 400 238 104 218 156
 346 290 259 403 291 318 374 475 444 100 339 539 200 439 359 348  56  35
  31 533 247 231 399 252  86  17 453 325 244 543 472 416 157 190 229 128
 516 523 129 127 356 114   1 385  39 396 441 538 124 341 161 201 287 118
 361 520  85  75 243 440 140 159   9 236 410 466 147  46 502 299 136  98
  36 338 175 406 174  96 512 137 188 470  48 387 478 465 412  42   5 434
 308  21 125 115 223 420 529 178 130   2 246 377 277 432 384 284  49 265
  79 479 120 106 496 116 171 322 253 260  26 176 170 495 126 330 364 383
 103 342 548 526 347 391 504 544 306  58  44 534 550 473 446  72 143  47
  93 460   0 438 323 445 531  53 324 349 312 224 452 490 447 310 366  70
 270 389 192 327  34 462 217 511 255  99 241 554 168 402 211 292 285 508
 212 417 207 300 429 179 553  82 431 107 205 203 503 216 196 345 249 109
 123 528 276 237 117 435   6 202 149 409  11 426 144 169 263 184 493  81
 436 450 199 112 545 380  41 501 362  68 536  54  27 491 294 390 386 333
 268 507 422 443  73 369 332 382 195 225 278 142 275 535 148 423 283 340
 113  71  16 180 164 233 158 206 226  67 261  25 379 488 464 408 421 146
  24 487 484 134 279  84 395 394 424 293 407 301 295 456 370 282 474 110
 371 220  88 315 530 414 519  20 547 273 108 427 314  90 552  60 397 187
 219  63 485  40 494 269 191 405  45 177 208  33 139  94  87 337 343 413
 248  15 411 367 145 525 242   3 320 132 476] [500 154  76 357 122  37 467  59 358 381 182   8 510 281 360 372 392 309
 160 459 418 442 303 266 517 186 468 239 250 368 551 355 499 302 296 549
  80 521  13 307 335 404 527  12 258 234  83 365  66 463  61 121 481  57
 267 245 193  62 101 197  97 433  91 419 524  95 483  50 222 317  51 227
 163 363 150  22 298 235 210 509 133 352 271  19 141 515 209 311 451  92
 131  74 354 183 498 172 537 304 344 119 262 398 373  77 336 167 272 513
 155  10   4 425 497 166 329 328 428 215 458 518 454 514 448 152 286 189
 254 316 449 251 542 214 430 162 228 415 541  78 232  55 505 153 540 532
 461 185 555 482 378 331 274 334 151 506  64 350 492 437 486 477 353 289
 288  28 376 480 138]
[ 55 529  87 256  56 134 244 517 479  82 428  53 264 327  13 486 279 406
  91  43 274 474 465   0 196 193 117 525 285 148 536 442 453 315  11 544
 504 392 291 243 220 516 314  71 429  14 399 328  63 160  73 485 306 116
 528 115 423  95 434 283 376 191 258 493 308  58 339 334 401  15 519   7
 498 146 451 296 217 496 158 356  75 281  40 547 464 294 310  33 195  19
 190 204 181 419  54 377  99  98 407 502 466 530 340 395  64 477 300  57
 420 206 284 108  25 510  78 170  61 101 531 103 475 448 292 445 397 415
  23 472 408 455 192 252 184 398  72 343  67 234 446 176 242 424 212 293
 141 326 482 363 125 301  12 350 136 344 145  34 362 313 223 211 527  21
 270 447 267 444 309 484 276 280 543 149 439 175 265 142 533 490 261 329
 147 347 378 405 553 354  65 288 177  37 546 323 133 249 278  36 106  69
  38 422 413 382 260 457 186 104 233 450 154 216 100 524 489 156 250 132
 118 257 208 124  88  62 253 311  50 332 197 225 286 381   1 538 467 421
 500 441 228 189 545  86 307 266 411 162 353 537  70 366 268 430 297 129
 400 226 394 151 290 324 555 318  10 505 364 404  49 460 345 508 402 391
 359 114 456 200 201 277  31  68 110 163 303 138 393 348 203 385 139 213
 188  28 352  39 369  52 272 330 514 130  32 454  89 251 282 172 503 140
 371 373 540 102 410 199  16  24 357 227 331 299 325 174 128 383 458   2
 534 107 414 168 549 185  79 438 443 338 521  51 480 452 167 535 435 333
  45 506 495 126 194 302 351 150 367 214 161 539 388 507 426 219  35 523
 187 287 122 248 418 152 476  47 224 113 396 349 121 273 131 542 222  42
 210 263 526 368 425 159 483 232 305 491 511] [471 481 183 541 321 548 478 518 229 433 127 171  85  74 470 515 358 295
 182 337 550 202 259 360   5  90 119 247 440 487   4 312   3  20 387 236
   6 215 416 169 449 320 380  46 218  48 317 437  84 341  81  76 166 379
  83 335 289 271 499 207  29 365 165  94 123 355  18  44 463 386 461 205
 231 370 509  93 319 459 336  80 262 246  26 143  97 173  22   9 180  27
  66 372 298 375  92   8 155 255  59 254 120 304 532 501 409 112 105 135
 403 492 245 374 157 179 209 361 520 390 269 230 240  17 427 137  60 551
 431 468 554 322 178 432  41 513 164  77 237 462 488 384 241 346 473 497
 239 275 522 494  96 111 109 198 412 153 235 342  30 417 552 316 469 436
 238 389 512 221 144]


print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

0.762 accuracy with a standard deviation of 0.025


from sklearn.metrics import roc_curve
from sklearn.metrics import auc
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score


MLA = [model_0,model_1,model_2,model_3,model_4,model_5,model_6,model_7,eclf]


MLA_columns = []
MLA_compare = pd.DataFrame(columns = MLA_columns)

row_index = 0
for alg in MLA:  
    
    predicted = alg.fit(X_trainValid, y_trainValid).predict(X_test)
    fp, tp, th = roc_curve(y_test, predicted)
    MLA_name = alg.__class__.__name__
    MLA_compare.loc[row_index,'MLA used'] = MLA_name
    MLA_compare.loc[row_index, 'Train Accuracy'] = round(alg.score(X_trainValid, y_trainValid), 4)
    MLA_compare.loc[row_index, 'Test Accuracy'] = round(alg.score(X_test, y_test), 4)
    MLA_compare.loc[row_index, 'Precission'] = precision_score(y_test, predicted)
    MLA_compare.loc[row_index, 'Recall'] = recall_score(y_test, predicted)
    MLA_compare.loc[row_index, 'AUC'] = auc(fp, tp)
    MLA_compare.loc[row_index, 'F1-Score'] = f1_score(y_test, predicted)
    
    row_index+=1
    
MLA_compare.sort_values(by = ['Test Accuracy'], ascending = False, inplace = True)
MLA_compare


# Creating plot to show the ROC for all MLA
index = 1
for alg in MLA:
    
    
    predicted = alg.fit(X_trainValid, y_trainValid).predict(X_test)
    fp, tp, th = roc_curve(y_test, predicted)
    roc_auc_mla = auc(fp, tp)
    MLA_name = alg.__class__.__name__
    plt.plot(fp, tp, lw=2, alpha=0.3, label='ROC %s (AUC = %0.2f)'  % (MLA_name, roc_auc_mla))
   
    index+=1

plt.title('ROC Curve comparison')
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.plot([0,1],[0,1],'r--')
plt.xlim([0,1])
plt.ylim([0,1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')    
plt.show()


X_trainValid = pd.concat([X_train_imbalance,X_valid],axis=0)


y_trainValid = pd.concat([y_train_imbalance,y_valid],axis=0)


MLA_columns = []
MLA_compare = pd.DataFrame(columns = MLA_columns)

row_index = 0
for alg in MLA:  
    
    predicted = alg.fit(X_trainValid, y_trainValid).predict(X_test)
    fp, tp, th = roc_curve(y_test, predicted)
    MLA_name = alg.__class__.__name__
    MLA_compare.loc[row_index,'MLA used'] = MLA_name
    MLA_compare.loc[row_index, 'Train Accuracy'] = round(alg.score(X_trainValid, y_trainValid), 4)
    MLA_compare.loc[row_index, 'Test Accuracy'] = round(alg.score(X_test, y_test), 4)
    MLA_compare.loc[row_index, 'Precission'] = precision_score(y_test, predicted)
    MLA_compare.loc[row_index, 'Recall'] = recall_score(y_test, predicted)
    MLA_compare.loc[row_index, 'AUC'] = auc(fp, tp)
    MLA_compare.loc[row_index, 'F1-Score'] = f1_score(y_test, predicted)
    
    row_index+=1
    
MLA_compare.sort_values(by = ['Test Accuracy'], ascending = False, inplace = True)
MLA_compare


data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 477 entries, 0 to 478
Data columns (total 13 columns):
 #   Column                             Non-Null Count  Dtype
---  ------                             --------------  -----
 0   Performance-Based Errors           477 non-null    int64
 1   Judgment & Decision-Making Errors  477 non-null    int64
 2   Violations                         477 non-null    int64
 3   Physical Environment               477 non-null    int64
 4   Inadequate Supervision             477 non-null    int64
 5   Technology Failure                 477 non-null    int64
 6   Acts                               477 non-null    int64
 7   Preconditions                      477 non-null    int64
 8   Supervision                        477 non-null    int64
 9   Organization                       477 non-null    int64
 10  Fatal or Serious                   477 non-null    int64
 11  Flight Segment 1=Taxi              477 non-null    int64
 12  91=1/121=0                         477 non-null    int64
dtypes: int64(13)
memory usage: 68.3 KB


data.drop(['Flight Segment 1=Taxi'],axis=1,inplace=True)


from mlxtend.frequent_patterns import apriori, association_rules


apriori = apriori(data, min_support = 0.2, use_colnames = True, verbose = 1)
apriori.sort_values(by='support',ascending=False).head(30)

Processing 9 combinations | Sampling itemset size 3

C:\ProgramData\Anaconda3\lib\site-packages\mlxtend\frequent_patterns\fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type
  warnings.warn(


rules = association_rules(apriori, metric = "support", min_threshold = 0.1)
rules.head(30)


rules[rules['lift'] >= 1].sort_values(by='lift',ascending=False).head(30)


plt.scatter(rules['support'], rules['confidence'], alpha=0.5)
plt.xlabel('support')
plt.ylabel('confidence')
plt.title('Support vs Confidence')
plt.show()


plt.scatter(rules['support'], rules['lift'], alpha=0.5)
plt.xlabel('support')
plt.ylabel('lift')
plt.title('Support vs Lift')
plt.show()


fit = np.polyfit(rules['lift'], rules['confidence'], 1)
fit_fn = np.poly1d(fit)
plt.plot(rules['lift'], rules['confidence'], 'yo', rules['lift'], 
 fit_fn(rules['lift']))

[<matplotlib.lines.Line2D at 0x26284d24460>,
 <matplotlib.lines.Line2D at 0x26284d24430>]

	Performance-Based Errors	Judgment & Decision-Making Errors	Violations	Teamwork	Mental Awareness	State of Mind	Physical Problems	Sensory Misperception	Physical Environment	Technological Environment	...	Technology Failure	Acts	Preconditions	Supervision	Organization	Fatal or Serious	Flight Segment 1=Taxi	91=1/121=0	91= namely general aviation pilots	121 = qualified pilots
count	479.000000	479.000000	479.000000	479.000000	479.000000	479.000000	479.000000	479.000000	479.000000	479.000000	...	479.000000	479.000000	479.000000	479.000000	479.000000	479.000000	479.000000	479.000000	0.0	0.0
mean	0.480167	0.127349	0.070981	0.035491	0.016701	0.004175	0.039666	0.018789	0.162839	0.002088	...	0.319415	0.638831	0.252610	0.045929	0.048017	0.354906	4.951983	0.678497	NaN	NaN
std	0.500129	0.333712	0.257062	0.185210	0.128284	0.064549	0.195377	0.135922	0.369605	0.045691	...	0.466738	0.480842	0.434963	0.209550	0.214025	0.478985	2.026960	0.467542	NaN	NaN
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	NaN	NaN
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000	0.000000	NaN	NaN
50%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	6.000000	1.000000	NaN	NaN
75%	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	1.000000	1.000000	1.000000	0.000000	0.000000	1.000000	7.000000	1.000000	NaN	NaN
max	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	...	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	7.000000	1.000000	NaN	NaN

	Performance-Based Errors	Judgment & Decision-Making Errors	Violations	Physical Environment	Inadequate Supervision	Technology Failure	Acts	Preconditions	Supervision	Organization	Fatal or Serious	Flight Segment 1=Taxi
0	1.045038	-0.382929	-0.277037	-0.442141	-0.214599	-0.687184	0.754373	-0.582999	-0.219890	-0.22508	1.012065	0.683880
1	-0.956903	2.611450	-0.277037	-0.442141	-0.214599	-0.687184	0.754373	-0.582999	-0.219890	-0.22508	-0.467266	0.683880
2	1.045038	-0.382929	-0.277037	-0.442141	-0.214599	-0.687184	0.754373	-0.582999	-0.219890	-0.22508	1.012065	0.683880
3	-0.956903	-0.382929	-0.277037	-0.442141	-0.214599	1.455214	-1.325604	-0.582999	-0.219890	-0.22508	1.012065	0.683880
4	1.045038	-0.382929	-0.277037	-0.442141	-0.214599	-0.687184	0.754373	-0.582999	-0.219890	-0.22508	1.012065	0.683880
...	...	...	...	...	...	...	...	...	...	...	...	...
472	-0.956903	-0.382929	3.609628	-0.442141	-0.214599	-0.687184	0.754373	-0.582999	-0.219890	-0.22508	0.025844	-1.462244
473	-0.956903	-0.382929	-0.277037	-0.442141	-0.214599	-0.687184	-1.325604	-0.582999	-0.219890	-0.22508	-0.467266	-1.462244
474	-0.956903	-0.382929	-0.277037	2.261722	-0.214599	-0.687184	-1.325604	1.715269	-0.219890	-0.22508	0.025844	-1.462244
475	-0.956903	-0.382929	-0.277037	2.261722	-0.214599	-0.687184	-1.325604	1.715269	-0.219890	-0.22508	0.025844	-1.462244
476	-0.956903	-0.382929	-0.277037	-0.442141	4.659859	1.455214	-1.325604	-0.582999	4.547727	-0.22508	0.518955	-1.462244

	MLA used	Train Accuracy	Test Accuracy	Precission	Recall	AUC	F1-Score
1	RandomForestClassifier	0.8500	0.8125	0.611111	0.846154	0.823077	0.709677
2	XGBClassifier	0.8457	0.8021	0.600000	0.807692	0.803846	0.688525
3	GradientBoostingClassifier	0.8174	0.8021	0.606061	0.769231	0.791758	0.677966
4	BaggingClassifier	0.8457	0.8021	0.594595	0.846154	0.815934	0.698413
6	DecisionTreeClassifier	0.8500	0.8021	0.600000	0.807692	0.803846	0.688525
8	VotingClassifier	0.8478	0.8021	0.600000	0.807692	0.803846	0.688525
7	BernoulliNB	0.6304	0.7708	0.576923	0.576923	0.709890	0.576923
0	LogisticRegression	0.6870	0.7604	0.548387	0.653846	0.726923	0.596491
5	SVC	0.7717	0.6979	0.461538	0.692308	0.696154	0.553846

	MLA used	Train Accuracy	Test Accuracy	Precission	Recall	AUC	F1-Score
2	XGBClassifier	0.8478	0.8125	0.633333	0.730769	0.786813	0.678571
3	GradientBoostingClassifier	0.8031	0.8125	0.642857	0.692308	0.774725	0.666667
8	VotingClassifier	0.8504	0.8125	0.633333	0.730769	0.786813	0.678571
1	RandomForestClassifier	0.8530	0.8021	0.600000	0.807692	0.803846	0.688525
6	DecisionTreeClassifier	0.8530	0.7917	0.593750	0.730769	0.772527	0.655172
4	BaggingClassifier	0.8425	0.7812	0.571429	0.769231	0.777473	0.655738
7	BernoulliNB	0.6772	0.7708	0.583333	0.538462	0.697802	0.560000
0	LogisticRegression	0.6772	0.7500	0.555556	0.384615	0.635165	0.454545
5	SVC	0.7533	0.7396	0.517241	0.576923	0.688462	0.545455

	support	itemsets
5	0.681342	(91=1/121=0)
2	0.637317	(Acts)
10	0.515723	(Acts, 91=1/121=0)
0	0.477987	(Performance-Based Errors)
6	0.477987	(Acts, Performance-Based Errors)
7	0.387841	(Performance-Based Errors, 91=1/121=0)
12	0.387841	(Acts, Performance-Based Errors, 91=1/121=0)
4	0.356394	(Fatal or Serious)
1	0.320755	(Technology Failure)
3	0.253669	(Preconditions)
11	0.245283	(Fatal or Serious, 91=1/121=0)
9	0.241090	(Fatal or Serious, Acts)
8	0.218029	(Technology Failure, 91=1/121=0)
13	0.205451	(Fatal or Serious, Acts, 91=1/121=0)

	EventId	Performance-Based Errors	Judgment & Decision-Making Errors	...	Technology Failure	Acts	Fatal or Serious	Flight Segment 1=Taxi	91=1/121=0	91= namely general aviation pilots	121 = qualified pilots
0	20060629X00856	1	0	...	0	1	0	7	1	NaN	NaN
1	20060113X00068	0	1	...	0	1	1	4	1	NaN	NaN
2	20060929X01431	1	0	...	0	1	0	7	1	NaN	NaN
3	20070109X00026	0	0	...	1	0	0	7	1	NaN	NaN
4	20060719X00965	1	0	...	0	1	0	7	1	NaN	NaN

	EventId	Violations	Physical Environment	...	Technology Failure	Acts	Preconditions	Supervision	Fatal or Serious	Flight Segment 1=Taxi	91= namely general aviation pilots	121 = qualified pilots
474	20150717X41748	1	0	...	0	1	0	0	1	5	NaN	NaN
475	20151022X65901	0	0	...	0	0	0	0	1	4	NaN	NaN
476	20151130X61148	0	1	...	0	0	1	0	1	5	NaN	NaN
477	20151213X84149	0	1	...	0	0	1	0	1	5	NaN	NaN
478	20150605X52542	0	0	...	1	0	0	1	0	6	NaN	NaN

	EventId	Performance-Based Errors	Judgment & Decision-Making Errors	Violations	Teamwork	Mental Awareness	State of Mind	Physical Problems	Sensory Misperception	Physical Environment	...	Climate/ Culture Influences	Policy & Process Issues	Technology Failure	Acts	Preconditions	Supervision	Organization	Fatal or Serious	Flight Segment 1=Taxi	91=1/121=0
157	20090604X25647	1	0	0	0	0	0	0	0	0	...	0	0	0	1	0	0	0	0	6	0
303	20110518X94643	1	0	0	0	0	0	0	0	0	...	0	0	0	1	0	0	0	0	6	0

	Performance-Based Errors	Judgment & Decision-Making Errors	Technology Failure	Acts	Fatal or Serious	Flight Segment 1=Taxi	91=1/121=0
0	1	0	0	1	0	7	1
1	0	1	0	1	1	4	1
2	1	0	0	1	0	7	1
3	0	0	1	0	0	7	1
4	1	0	0	1	0	7	1

	Performance-Based Errors	Judgment & Decision-Making Errors	Violations	Physical Environment	Inadequate Supervision	Technology Failure	Acts	Preconditions	Supervision	Organization	Flight Segment 1=Taxi	91=1/121=0
429	0	0	0	0	0	1	0	0	0	0	2	1
333	1	0	0	0	0	0	1	0	0	0	6	1
109	0	0	0	0	0	1	0	0	0	0	2	1
93	0	0	0	1	1	0	0	1	1	0	5	0
229	1	0	0	0	0	0	1	0	0	0	6	0
...	...	...	...	...	...	...	...	...	...	...	...	...
63	0	1	0	1	0	0	1	1	0	0	7	1
328	1	0	0	0	0	0	1	0	0	0	5	1
339	0	0	1	0	0	0	1	0	0	0	7	1
11	0	0	0	0	0	1	0	0	0	0	7	1
353	0	0	0	1	0	0	0	1	0	0	5	0

	Performance-Based Errors	Judgment & Decision-Making Errors	Violations	Physical Environment	Inadequate Supervision	Technology Failure	Acts	Preconditions	Supervision	Organization	Flight Segment 1=Taxi	91=1/121=0
114	0	0	1	0	0	0	1	0	0	0	2	1
440	1	0	0	0	0	0	1	0	0	0	6	1
143	0	0	0	1	0	0	0	1	0	0	2	0
226	1	0	0	0	0	0	1	0	0	0	6	0
268	0	0	1	0	0	1	1	1	0	0	2	1
278	0	0	0	0	0	1	0	0	0	0	7	0
138	0	0	0	1	0	0	0	1	0	0	3	0
31	0	0	0	1	0	0	0	1	0	0	4	1
91	0	0	0	0	0	1	0	0	0	1	2	0
406	0	0	0	0	0	1	0	0	0	0	7	1
100	1	0	0	0	0	0	1	0	0	0	2	1
128	1	0	0	0	0	0	1	0	0	0	2	1
315	1	0	0	0	0	0	1	0	0	0	6	1
104	1	0	0	0	0	0	1	0	0	0	4	1
212	0	0	0	0	1	1	0	0	1	0	7	0
169	0	0	0	0	0	1	0	0	0	0	2	1
190	1	0	0	0	0	0	1	0	0	0	6	1
260	0	0	0	0	0	1	0	0	0	0	7	1
438	1	1	0	0	0	0	1	0	0	0	7	1
391	0	0	0	0	0	1	0	0	0	0	7	1
418	0	0	0	0	0	0	0	1	0	0	6	0
133	1	0	0	0	0	0	1	0	0	0	7	0
317	0	0	1	0	0	1	1	0	0	0	7	1
334	0	0	1	0	0	1	1	0	0	0	2	1
117	0	0	0	0	0	1	0	0	0	0	7	1
238	1	0	0	1	0	0	1	1	0	0	7	1
454	0	0	0	0	0	1	0	0	0	0	4	0
213	1	0	0	0	0	0	1	1	0	0	2	0
130	1	0	0	0	0	0	1	1	0	0	7	1
346	1	0	0	0	0	0	1	0	0	0	7	1
349	0	0	0	0	0	1	0	0	0	0	2	1
180	1	0	0	0	0	0	1	0	0	0	7	1
210	0	0	0	1	0	0	0	1	0	0	3	0
419	0	0	0	1	0	0	0	1	0	0	7	1
17	0	1	0	1	0	0	1	1	0	0	6	1
184	0	0	0	0	0	1	0	0	0	0	5	1
351	0	0	0	0	0	1	0	0	0	0	7	1
478	0	0	0	0	1	1	0	0	1	0	6	0
408	1	0	0	0	0	1	1	0	0	0	2	1
13	1	0	0	0	0	0	1	0	0	0	6	1
211	0	0	0	1	0	0	0	1	0	0	4	0
38	1	0	0	0	0	0	1	0	0	0	5	1
72	1	0	0	0	0	0	1	0	0	0	5	1
2	1	0	0	0	0	0	1	0	0	0	7	1
387	0	0	0	0	0	1	0	0	0	0	7	1
179	1	0	0	0	0	0	1	0	0	1	2	1
308	0	0	0	0	0	0	0	1	0	0	4	0
412	1	0	0	0	0	0	1	0	0	0	7	0

	Performance-Based Errors	Judgment & Decision-Making Errors	Violations	Physical Environment	Inadequate Supervision	Technology Failure	Acts	Preconditions	Supervision	Organization	Flight Segment 1=Taxi	91=1/121=0
455	1	0	0	0	0	0	1	0	0	0	7	0
399	1	0	0	0	0	0	1	0	0	0	7	1
166	1	0	0	0	0	0	1	0	0	1	6	1
188	1	0	0	1	0	0	1	1	0	0	7	1
53	0	0	0	0	0	1	0	0	0	0	7	0
...	...	...	...	...	...	...	...	...	...	...	...	...
4	1	0	0	0	0	0	1	0	0	0	7	1
234	0	0	0	0	0	0	0	1	0	0	5	0
8	1	0	0	1	0	0	1	1	0	0	2	1
468	1	0	0	0	0	0	1	0	0	0	7	1
335	0	1	0	0	0	1	1	0	0	0	2	1

	Label	Prediction
114	1	1
440	0	1
143	1	0
226	0	0
268	0	1
278	0	0
138	0	1
31	0	0
91	0	0
406	0	0
100	1	1
128	0	1
315	1	1
104	1	0
212	0	0
169	1	0
190	1	1
260	0	0
438	0	0
391	0	0
418	1	1
133	0	0
317	0	0
334	1	1
117	0	0
238	0	0
454	1	1
213	0	0
130	0	0
346	0	0
349	0	0
180	0	0
210	0	1
419	0	1
17	1	0
184	1	0
351	0	0
478	0	0
408	1	1
13	0	1
211	1	1
38	1	1
72	1	1
2	0	0
387	0	0
179	0	0
308	1	0
412	0	0

	Value
Flight Segment 1=Taxi	0.499536
91=1/121=0	0.142917
Preconditions	0.056012
Physical Environment	0.052905
Performance-Based Errors	0.052388
Technology Failure	0.052089
Supervision	0.044917
Violations	0.036851
Judgment & Decision-Making Errors	0.029812
Acts	0.023049
Organization	0.009523
Inadequate Supervision	0.000000

	Label	Prediction
114	1	1
440	0	1
143	1	1
226	0	0
268	0	1
278	0	0
138	0	1
31	0	0
91	0	0
406	0	0
100	1	1
128	0	1
315	1	1
104	1	0
212	0	0
169	1	0
190	1	1
260	0	0
438	0	0
391	0	0
418	1	1
133	0	0
317	0	0
334	1	1
117	0	0
238	0	0
454	1	1
213	0	1
130	0	0
346	0	0
349	0	0
180	0	0
210	0	1
419	0	0
17	1	0
184	1	0
351	0	0
478	0	0
408	1	1
13	0	1
211	1	1
38	1	1
72	1	1
2	0	0
387	0	0
179	0	0
308	1	0
412	0	0

	Performance-Based Errors	Judgment & Decision-Making Errors	Technology Failure	Acts	Fatal or Serious	Flight Segment 1=Taxi	91=1/121=0
0	1	0	0	1	0	7	1
1	0	1	0	1	1	4	1
2	1	0	0	1	0	7	1
3	0	0	1	0	0	7	1
4	1	0	0	1	0	7	1

	Performance-Based Errors	Judgment & Decision-Making Errors	Violations	Physical Environment	Inadequate Supervision	Technology Failure	Acts	Preconditions	Supervision	Organization	Flight Segment 1=Taxi	91=1/121=0
429	0	0	0	0	0	1	0	0	0	0	2	1
333	1	0	0	0	0	0	1	0	0	0	6	1
109	0	0	0	0	0	1	0	0	0	0	2	1
93	0	0	0	1	1	0	0	1	1	0	5	0
229	1	0	0	0	0	0	1	0	0	0	6	0
...	...	...	...	...	...	...	...	...	...	...	...	...
63	0	1	0	1	0	0	1	1	0	0	7	1
328	1	0	0	0	0	0	1	0	0	0	5	1
339	0	0	1	0	0	0	1	0	0	0	7	1
11	0	0	0	0	0	1	0	0	0	0	7	1
353	0	0	0	1	0	0	0	1	0	0	5	0

	Performance-Based Errors	Judgment & Decision-Making Errors	Violations	Physical Environment	Inadequate Supervision	Technology Failure	Acts	Preconditions	Supervision	Organization	Flight Segment 1=Taxi	91=1/121=0
114	0	0	1	0	0	0	1	0	0	0	2	1
440	1	0	0	0	0	0	1	0	0	0	6	1
143	0	0	0	1	0	0	0	1	0	0	2	0
226	1	0	0	0	0	0	1	0	0	0	6	0
268	0	0	1	0	0	1	1	1	0	0	2	1
278	0	0	0	0	0	1	0	0	0	0	7	0
138	0	0	0	1	0	0	0	1	0	0	3	0
31	0	0	0	1	0	0	0	1	0	0	4	1
91	0	0	0	0	0	1	0	0	0	1	2	0
406	0	0	0	0	0	1	0	0	0	0	7	1
100	1	0	0	0	0	0	1	0	0	0	2	1
128	1	0	0	0	0	0	1	0	0	0	2	1
315	1	0	0	0	0	0	1	0	0	0	6	1
104	1	0	0	0	0	0	1	0	0	0	4	1
212	0	0	0	0	1	1	0	0	1	0	7	0
169	0	0	0	0	0	1	0	0	0	0	2	1
190	1	0	0	0	0	0	1	0	0	0	6	1
260	0	0	0	0	0	1	0	0	0	0	7	1
438	1	1	0	0	0	0	1	0	0	0	7	1
391	0	0	0	0	0	1	0	0	0	0	7	1
418	0	0	0	0	0	0	0	1	0	0	6	0
133	1	0	0	0	0	0	1	0	0	0	7	0
317	0	0	1	0	0	1	1	0	0	0	7	1
334	0	0	1	0	0	1	1	0	0	0	2	1
117	0	0	0	0	0	1	0	0	0	0	7	1
238	1	0	0	1	0	0	1	1	0	0	7	1
454	0	0	0	0	0	1	0	0	0	0	4	0
213	1	0	0	0	0	0	1	1	0	0	2	0
130	1	0	0	0	0	0	1	1	0	0	7	1
346	1	0	0	0	0	0	1	0	0	0	7	1
349	0	0	0	0	0	1	0	0	0	0	2	1
180	1	0	0	0	0	0	1	0	0	0	7	1
210	0	0	0	1	0	0	0	1	0	0	3	0
419	0	0	0	1	0	0	0	1	0	0	7	1
17	0	1	0	1	0	0	1	1	0	0	6	1
184	0	0	0	0	0	1	0	0	0	0	5	1
351	0	0	0	0	0	1	0	0	0	0	7	1
478	0	0	0	0	1	1	0	0	1	0	6	0
408	1	0	0	0	0	1	1	0	0	0	2	1
13	1	0	0	0	0	0	1	0	0	0	6	1
211	0	0	0	1	0	0	0	1	0	0	4	0
38	1	0	0	0	0	0	1	0	0	0	5	1
72	1	0	0	0	0	0	1	0	0	0	5	1
2	1	0	0	0	0	0	1	0	0	0	7	1
387	0	0	0	0	0	1	0	0	0	0	7	1
179	1	0	0	0	0	0	1	0	0	1	2	1
308	0	0	0	0	0	0	0	1	0	0	4	0
412	1	0	0	0	0	0	1	0	0	0	7	0

	Performance-Based Errors	Judgment & Decision-Making Errors	Violations	Physical Environment	Inadequate Supervision	Technology Failure	Acts	Preconditions	Supervision	Organization	Flight Segment 1=Taxi	91=1/121=0
455	1	0	0	0	0	0	1	0	0	0	7	0
399	1	0	0	0	0	0	1	0	0	0	7	1
166	1	0	0	0	0	0	1	0	0	1	6	1
188	1	0	0	1	0	0	1	1	0	0	7	1
53	0	0	0	0	0	1	0	0	0	0	7	0
...	...	...	...	...	...	...	...	...	...	...	...	...
4	1	0	0	0	0	0	1	0	0	0	7	1
234	0	0	0	0	0	0	0	1	0	0	5	0
8	1	0	0	1	0	0	1	1	0	0	2	1
468	1	0	0	0	0	0	1	0	0	0	7	1
335	0	1	0	0	0	1	1	0	0	0	2	1

	Label	Prediction
114	1	1
440	0	1
143	1	0
226	0	0
268	0	1
278	0	0
138	0	1
31	0	0
91	0	0
406	0	0
100	1	1
128	0	1
315	1	1
104	1	0
212	0	0
169	1	0
190	1	1
260	0	0
438	0	0
391	0	0
418	1	1
133	0	0
317	0	0
334	1	1
117	0	0
238	0	0
454	1	1
213	0	0
130	0	0
346	0	0
349	0	0
180	0	0
210	0	1
419	0	1
17	1	0
184	1	0
351	0	0
478	0	0
408	1	1
13	0	1
211	1	1
38	1	1
72	1	1
2	0	0
387	0	0
179	0	0
308	1	0
412	0	0

	Label	Prediction
114	1	1
440	0	1
143	1	1
226	0	0
268	0	1
278	0	0
138	0	1
31	0	0
91	0	0
406	0	0
100	1	1
128	0	1
315	1	1
104	1	0
212	0	0
169	1	0
190	1	1
260	0	0
438	0	0
391	0	0
418	1	1
133	0	0
317	0	0
334	1	1
117	0	0
238	0	0
454	1	1
213	0	1
130	0	0
346	0	0
349	0	0
180	0	0
210	0	1
419	0	0
17	1	0
184	1	0
351	0	0
478	0	0
408	1	1
13	0	1
211	1	1
38	1	1
72	1	1
2	0	0
387	0	0
179	0	0
308	1	0
412	0	0

	Value
Flight Segment 1=Taxi	0.482800
91=1/121=0	0.098460
Technology Failure	0.075286
Preconditions	0.063597
Performance-Based Errors	0.061734
Violations	0.051583
Physical Environment	0.046682
Acts	0.041104
Judgment & Decision-Making Errors	0.033210
Supervision	0.016857
Organization	0.014778
Inadequate Supervision	0.013907

	Label	Prediction
114	1	1
440	0	1
143	1	0
226	0	0
268	0	1
278	0	0
138	0	1
31	0	1
91	0	0
406	0	0
100	1	1
128	0	1
315	1	1
104	1	1
212	0	0
169	1	1
190	1	1
260	0	0
438	0	0
391	0	0
418	1	1
133	0	0
317	0	0
334	1	1
117	0	0
238	0	0
454	1	1
213	0	1
130	0	0
346	0	0
349	0	1
180	0	0
210	0	1
419	0	0
17	1	1
184	1	1
351	0	0
478	0	0
408	1	1
13	0	1
211	1	1
38	1	1
72	1	1
2	0	0
387	0	0
179	0	1
308	1	1
412	0	0

	Performance-Based Errors	Judgment & Decision-Making Errors	Violations	Physical Environment	Inadequate Supervision	Technology Failure	Acts	Preconditions	Supervision	Organization	Flight Segment 1=Taxi	91=1/121=0
0	0	0	0	0	0	1	0	0	0	0	2	1
1	1	0	0	0	0	0	1	0	0	0	6	1
2	0	0	0	0	0	1	0	0	0	0	2	1
3	0	0	0	1	1	0	0	1	1	0	5	0
4	1	0	0	0	0	0	1	0	0	0	6	0
...	...	...	...	...	...	...	...	...	...	...	...	...
2	1	0	0	0	0	0	1	0	0	0	7	1
387	0	0	0	0	0	1	0	0	0	0	7	1
179	1	0	0	0	0	0	1	0	0	1	2	1
308	0	0	0	0	0	0	0	1	0	0	4	0
412	1	0	0	0	0	0	1	0	0	0	7	0

Context¶

Import Libraries For Overview & EDA¶

Get The Dataset¶

Overview Of The Dataset¶

Data Cleaning¶

Columns¶

91= namely general aviation pilots & 121 = qualified pilots¶

EventID¶

Performance-Based Errors¶

Judgment & Decision-Making Errors¶

Violations¶

Teamwork¶

Mental Awareness¶

State of Mind¶

Physical Problems¶

Sensory Misperception¶

Physical Environment¶

Technological Environment¶

Inadequate Supervision¶

Planned Inappropriate Operations¶

Supervisory Violations¶

Resource Problems¶

Personnel Selection & Staffing¶

Climate/ Culture Influences¶

Policy & Process Issues¶

Technology Failure¶

Acts¶

Preconditions¶

Supervision¶

Organization¶

Fatal or Serious¶

Flight Segment 1=Taxi¶

91=1/121=0¶

Drop Non-Important Columns¶

EDA: Exploratory Data Analysis¶

Auto EDA with Sweetviz¶

Train Valid Test Split¶

Imbalance Dataset¶

Balance Train Dataset¶

Predictions¶

Logistic Regression¶

K Nearest Neighbors¶

Decision Tree¶

Hyper Parameter For Decision Tree¶

Random Forest¶

Support Vector Machine¶

Bernoulli Naive Bayes¶

Bagging Classifier Algorithm¶

Gradient Boosting Classifier Algorithm¶

XGboost Classifier¶

Voting Classifier¶

Test Models¶

0. Logistic Regression¶

1. Random Forest Classifier¶

2. XGboost Classifier¶

3. Gradient Boosting Classifier¶

4. Bagging Classifier¶

5. Support Vector Machine¶

6. Decision Tree Classifier¶

7. Bernoulli Naive Bayes¶

8. Voting Classifier¶

Cross Validation: Evaluating Estimator Performance¶

Comparing Machine Learning Algorithms¶

Comparing Machine Learning Algorithms On Imbalance Dataset¶

Association Rule Mining: Apriori¶

END =)¶

	Performance-Based Errors	Judgment & Decision-Making Errors	Technology Failure	Acts	Fatal or Serious	Flight Segment 1=Taxi	91=1/121=0
0	1	0	0	1	0	7	1
1	0	1	0	1	1	4	1
2	1	0	0	1	0	7	1
3	0	0	1	0	0	7	1
4	1	0	0	1	0	7	1

	Performance-Based Errors	Judgment & Decision-Making Errors	Violations	Physical Environment	Inadequate Supervision	Technology Failure	Acts	Preconditions	Supervision	Organization	Flight Segment 1=Taxi	91=1/121=0
429	0	0	0	0	0	1	0	0	0	0	2	1
333	1	0	0	0	0	0	1	0	0	0	6	1
109	0	0	0	0	0	1	0	0	0	0	2	1
93	0	0	0	1	1	0	0	1	1	0	5	0
229	1	0	0	0	0	0	1	0	0	0	6	0
...	...	...	...	...	...	...	...	...	...	...	...	...
63	0	1	0	1	0	0	1	1	0	0	7	1
328	1	0	0	0	0	0	1	0	0	0	5	1
339	0	0	1	0	0	0	1	0	0	0	7	1
11	0	0	0	0	0	1	0	0	0	0	7	1
353	0	0	0	1	0	0	0	1	0	0	5	0

	Performance-Based Errors	Judgment & Decision-Making Errors	Violations	Physical Environment	Inadequate Supervision	Technology Failure	Acts	Preconditions	Supervision	Organization	Flight Segment 1=Taxi	91=1/121=0
114	0	0	1	0	0	0	1	0	0	0	2	1
440	1	0	0	0	0	0	1	0	0	0	6	1
143	0	0	0	1	0	0	0	1	0	0	2	0
226	1	0	0	0	0	0	1	0	0	0	6	0
268	0	0	1	0	0	1	1	1	0	0	2	1
278	0	0	0	0	0	1	0	0	0	0	7	0
138	0	0	0	1	0	0	0	1	0	0	3	0
31	0	0	0	1	0	0	0	1	0	0	4	1
91	0	0	0	0	0	1	0	0	0	1	2	0
406	0	0	0	0	0	1	0	0	0	0	7	1
100	1	0	0	0	0	0	1	0	0	0	2	1
128	1	0	0	0	0	0	1	0	0	0	2	1
315	1	0	0	0	0	0	1	0	0	0	6	1
104	1	0	0	0	0	0	1	0	0	0	4	1
212	0	0	0	0	1	1	0	0	1	0	7	0
169	0	0	0	0	0	1	0	0	0	0	2	1
190	1	0	0	0	0	0	1	0	0	0	6	1
260	0	0	0	0	0	1	0	0	0	0	7	1
438	1	1	0	0	0	0	1	0	0	0	7	1
391	0	0	0	0	0	1	0	0	0	0	7	1
418	0	0	0	0	0	0	0	1	0	0	6	0
133	1	0	0	0	0	0	1	0	0	0	7	0
317	0	0	1	0	0	1	1	0	0	0	7	1
334	0	0	1	0	0	1	1	0	0	0	2	1
117	0	0	0	0	0	1	0	0	0	0	7	1
238	1	0	0	1	0	0	1	1	0	0	7	1
454	0	0	0	0	0	1	0	0	0	0	4	0
213	1	0	0	0	0	0	1	1	0	0	2	0
130	1	0	0	0	0	0	1	1	0	0	7	1
346	1	0	0	0	0	0	1	0	0	0	7	1
349	0	0	0	0	0	1	0	0	0	0	2	1
180	1	0	0	0	0	0	1	0	0	0	7	1
210	0	0	0	1	0	0	0	1	0	0	3	0
419	0	0	0	1	0	0	0	1	0	0	7	1
17	0	1	0	1	0	0	1	1	0	0	6	1
184	0	0	0	0	0	1	0	0	0	0	5	1
351	0	0	0	0	0	1	0	0	0	0	7	1
478	0	0	0	0	1	1	0	0	1	0	6	0
408	1	0	0	0	0	1	1	0	0	0	2	1
13	1	0	0	0	0	0	1	0	0	0	6	1
211	0	0	0	1	0	0	0	1	0	0	4	0
38	1	0	0	0	0	0	1	0	0	0	5	1
72	1	0	0	0	0	0	1	0	0	0	5	1
2	1	0	0	0	0	0	1	0	0	0	7	1
387	0	0	0	0	0	1	0	0	0	0	7	1
179	1	0	0	0	0	0	1	0	0	1	2	1
308	0	0	0	0	0	0	0	1	0	0	4	0
412	1	0	0	0	0	0	1	0	0	0	7	0

	Performance-Based Errors	Judgment & Decision-Making Errors	Violations	Physical Environment	Inadequate Supervision	Technology Failure	Acts	Preconditions	Supervision	Organization	Flight Segment 1=Taxi	91=1/121=0
455	1	0	0	0	0	0	1	0	0	0	7	0
399	1	0	0	0	0	0	1	0	0	0	7	1
166	1	0	0	0	0	0	1	0	0	1	6	1
188	1	0	0	1	0	0	1	1	0	0	7	1
53	0	0	0	0	0	1	0	0	0	0	7	0
...	...	...	...	...	...	...	...	...	...	...	...	...
4	1	0	0	0	0	0	1	0	0	0	7	1
234	0	0	0	0	0	0	0	1	0	0	5	0
8	1	0	0	1	0	0	1	1	0	0	2	1
468	1	0	0	0	0	0	1	0	0	0	7	1
335	0	1	0	0	0	1	1	0	0	0	2	1

	Label	Prediction
114	1	1
440	0	1
143	1	0
226	0	0
268	0	1
278	0	0
138	0	1
31	0	0
91	0	0
406	0	0
100	1	1
128	0	1
315	1	1
104	1	0
212	0	0
169	1	0
190	1	1
260	0	0
438	0	0
391	0	0
418	1	1
133	0	0
317	0	0
334	1	1
117	0	0
238	0	0
454	1	1
213	0	0
130	0	0
346	0	0
349	0	0
180	0	0
210	0	1
419	0	1
17	1	0
184	1	0
351	0	0
478	0	0
408	1	1
13	0	1
211	1	1
38	1	1
72	1	1
2	0	0
387	0	0
179	0	0
308	1	0
412	0	0

	Label	Prediction
114	1	1
440	0	1
143	1	1
226	0	0
268	0	1
278	0	0
138	0	1
31	0	0
91	0	0
406	0	0
100	1	1
128	0	1
315	1	1
104	1	0
212	0	0
169	1	0
190	1	1
260	0	0
438	0	0
391	0	0
418	1	1
133	0	0
317	0	0
334	1	1
117	0	0
238	0	0
454	1	1
213	0	1
130	0	0
346	0	0
349	0	0
180	0	0
210	0	1
419	0	0
17	1	0
184	1	0
351	0	0
478	0	0
408	1	1
13	0	1
211	1	1
38	1	1
72	1	1
2	0	0
387	0	0
179	0	0
308	1	0
412	0	0

	Label	Prediction
114	1	1
440	0	1
143	1	0
226	0	0
268	0	1
278	0	0
138	0	1
31	0	1
91	0	0
406	0	0
100	1	1
128	0	1
315	1	1
104	1	1
212	0	0
169	1	1
190	1	1
260	0	0
438	0	0
391	0	0
418	1	1
133	0	0
317	0	0
334	1	1
117	0	0
238	0	0
454	1	1
213	0	1
130	0	0
346	0	0
349	0	1
180	0	0
210	0	1
419	0	0
17	1	1
184	1	1
351	0	0
478	0	0
408	1	1
13	0	1
211	1	1
38	1	1
72	1	1
2	0	0
387	0	0
179	0	1
308	1	1
412	0	0

	Performance-Based Errors	Judgment & Decision-Making Errors	Violations	Physical Environment	Inadequate Supervision	Technology Failure	Acts	Preconditions	Supervision	Organization	Flight Segment 1=Taxi	91=1/121=0
0	0	0	0	0	0	1	0	0	0	0	2	1
1	1	0	0	0	0	0	1	0	0	0	6	1
2	0	0	0	0	0	1	0	0	0	0	2	1
3	0	0	0	1	1	0	0	1	1	0	5	0
4	1	0	0	0	0	0	1	0	0	0	6	0
...	...	...	...	...	...	...	...	...	...	...	...	...
2	1	0	0	0	0	0	1	0	0	0	7	1
387	0	0	0	0	0	1	0	0	0	0	7	1
179	1	0	0	0	0	0	1	0	0	1	2	1
308	0	0	0	0	0	0	0	1	0	0	4	0
412	1	0	0	0	0	0	1	0	0	0	7	0